关键词: 支持向量机, 核空间, 文本分类

Abstract: The accuracy of classification of SVM in a two-class classification problem would be decreased because of those promiscuous samples.KCNN-SVM is proposed in this paper as an improved NN-SVM algorithm，which prunes a sample according to their nearest neighbor’s class label as well as the average distance in kernel space between it and its k congener nearest neighbors.Experimental results show that KCNN-SVM algorithm is better than both SVM and NN-SVM in accuracy of classification and the total training and testing time is comparative to that of NN-SVM.

Key words: Support Vector Machine, kernel space, text categorization